True positive (TP): Difference between revisions

Latest revision as of 21:11, 17 March 2023

See also: Machine learning terms

Introduction

In machine learning, a true positive (TP) is an instance where the model correctly recognizes an instance of a class. In binary classification, true positive is when the model correctly predicts the positive class.

Consider a model trained to recognize pictures of cats. If it correctly recognizes such a picture as featuring a cat, this would be considered a true positive; meaning the model has correctly recognized the presence of an identifiable feature or characteristic (in this case, an image of a cat) within input data.

Example

Binary classification refers to two possible outcomes of a prediction. For instance, in medical diagnosis scenarios, positive and negative classes represent patients with and without certain diseases, respectively. A classification model attempts to predict whether a newly enrolled patient has this disease or not by analyzing certain features or attributes about them. Its prediction can either be positive or negative depending on whether it believes they already have it or not.

True positive occurs when a model accurately predicts a positive outcome for a patient with the disease. True positive is essential as it demonstrates the model's capability to accurately detect these instances in real-world applications. In many applications such as medical diagnosis or fraud detection, correctly recognizing positive instances is vital.

Explain Like I'm 5 (ELI5)

Imagine you're playing a game of catch with two balls: red and blue. Your objective is to catch the red ball every time it's thrown to you.

Now, if someone throws the red ball to you and you catch it, that is an impressive accomplishment. It signifies that you did your duty by catching the red ball as intended.

Machine learning also has things that we want to "catch" or "detect," such as images of cats or dogs. A true positive in machine learning means the computer program correctly identified an image as being a cat (or whatever we were trying to detect).

Just as our game of catch the red ball was a true positive, correctly identifying an image as a cat using machine learning is also a real success.

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+{{see also|Machine learning terms}}
 ==Introduction==
-Machine learning classification is the process of classifying data into distinct classes. When assessing a model's performance, it is essential to assess its capacity for correctly predicting each data instance's class. One important evaluation metric used in binary classification is True Positive (TP), which measures how many positive samples are correctly classified as positive by the model.
+In [[machine learning]], a [[true positive (TP)]] is an instance where the [[model]] correctly recognizes an instance of a [[class]]. In [[binary classification]], true positive is when the model correctly predicts the [[positive class]].
-==What is True Positive?==
+Consider a model trained to recognize pictures of cats. If it correctly recognizes such a picture as featuring a cat, this would be considered a true positive; meaning the model has correctly recognized the presence of an identifiable feature or characteristic (in this case, an image of a cat) within input data.
-True Positive is a statistic used in binary classification problems to measure the number of samples classified correctly as positive by a model. It's defined as the number of positive samples correctly predicted as such by the algorithm, or in other words: True Positive counts the samples belonging to the positive class that are correctly classified by the system.
-Consider a binary classification problem where we need to predict if someone has or does not have a disease based on some medical tests. In this scenario, the positive class represents those with the disease, while the negative class represents those without. If our model correctly predicts someone has the disease, and they actually do, then this prediction is known as a True Positive.
+==Example==
+Binary classification refers to two possible outcomes of a prediction. For instance, in medical diagnosis scenarios, [[positive class|positive]] and [[negative class]]es represent patients with and without certain diseases, respectively. A classification model attempts to predict whether a newly enrolled patient has this disease or not by analyzing certain [[features]] or attributes about them. Its prediction can either be positive or negative depending on whether it believes they already have it or not.
-==How is True Positive used in Evaluation?==
+True positive occurs when a model accurately predicts a positive outcome for a patient with the disease. True positive is essential as it demonstrates the model's capability to accurately detect these instances in real-world applications. In many applications such as medical diagnosis or fraud detection, correctly recognizing positive instances is vital.
-True Positive (TP) rate is an essential metric when evaluating a classification model's performance, particularly when misclassification has high costs. For instance, misclassifying patients with diseases as healthy can have dire repercussions; thus, it's vital to minimize False Negative (FN) rate--which measures the number of positive samples incorrectly classified as negative by the model--which measures false positive rates.
-True Positive is used in combination with other evaluation metrics to calculate the performance of a classification model. Common metrics used in binary classification include Precision, Recall and F1-score. Precision measures the proportion of True Positive samples predicted as positive by the model while Recall measures its proportion among actual members of that positive class. Finally, F1-score represents the harmonic mean between Precision and Recall.
+==Explain Like I'm 5 (ELI5)==
+Imagine you're playing a game of catch with two balls: red and blue. Your objective is to catch the red ball every time it's thrown to you.
-==Explain Like I'm 5 (ELI5)==
+Now, if someone throws the red ball to you and you catch it, that is an impressive accomplishment. It signifies that you did your duty by catching the red ball as intended.
-Machine learning, also known as deep reinforcement learning, is the process of teaching a computer to recognize objects by showing it what to search for. For instance, we could teach your machine to recognize cats and dogs by showing it multiple pictures of both and instructing it which one belongs where.
+Machine learning also has things that we want to "catch" or "detect," such as images of cats or dogs. A true positive in machine learning means the computer program correctly identified an image as being a cat (or whatever we were trying to detect).
-When the computer encounters a new picture, it attempts to guess whether it is of a cat or dog. If its guess is correct, we refer to this as a True Positive; meaning the computer was right in identifying what the image was.
+Just as our game of catch the red ball was a true positive, correctly identifying an image as a cat using machine learning is also a real success.
-Knowing how many True Positives a computer receives is important because it allows us to assess its learning progress. If the machine makes too many errors, we need to figure out why so that we can assist it in becoming better educated.
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]